The moment your AI agent starts a long-_running task, something inevitable happens: it forgets what it was doing.

You see this pattern everywhere:

A code review agent that loses track of which files it has already reviewed

A research agent that stops mid-deep_dive because context window fills up

A multi_step agent that completes step 3 but has no idea what step 2 produced

This isn't a memory problem. It's an architecture problem.

The Context Debt Problem

Every agent accumulates context debt — the gap between what it knows and what it needs to know.

Three layers cause this:

Working memory — What the agent holds in its active context

Episodic memory — What it remembers from previous turns

Shared memory — What other agents know but this one doesn't

When any layer fails, the agent loses continuity.…